Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/2712

Perceptually-inspired and edge-directed color image super-resolution

Authors Tai, Y.-W.
Tong, W.-S.
Tang, C.-K.
Issue Date 2006
Source Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, v. 2, 2006, p. 1948-1955
Summary Inspired by multi-scale tensor voting, a computational framework for perceptual grouping and segmentation, we propose an edge-directed technique for color image super-resolution given a single low-resolution color image. Our multi-scale technique combines the advantages of edge-directed, reconstruction-based and learning-based methods, and is unique in two ways. First, we consider simultaneously all the three color channels in our multi-scale tensor voting framework to produce a multi-scale edge representation to guide the process of high-resolution color image reconstruction, which is subject to the back projection constraint. Fine details are inferred without noticeable blurry or ringing artifacts. Second, the inference of high-resolution curves is achieved by multi-scale tensor voting, using the dense voting field as an edge-preserving smoothness prior which is derived geometrically without any time-consuming learning procedure. Qualitative and quantitative results indicate that our method produces convincing results in complex test cases typically used by state-of-the-art image super-resolution techniques. © 2006 IEEE.
Subjects
ISSN 1063-6919
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Language English
Format Conference paper
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